方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 在线学习× | 自监督学习× | 迁移学习× | |
|---|---|---|---|
| 领域 | 机器学习 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning | Machine learning |
| 起源年份≠ | 1958–2000s | 2018–2020 | 2010 (formalized); 1990s (early roots) |
| 提出者≠ | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) | LeCun, Y. and community (formalized ~2018–2020) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| 类型≠ | Learning paradigm (sequential model update) | Representation learning paradigm | Learning paradigm |
| 开创性文献≠ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ | LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 别名 | incremental learning, sequential learning, streaming learning, online machine learning | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| 相关≠ | 6 | 3 | 3 |
| 摘要≠ | Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight. | Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
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